COVID-19 Analysis

This short report analyzes some of the data from the repository by Johns Hopkins CCSE related to the COVID-19 pandemic.

Preliminaries

We have developed a simple Python module with a set of functions that can be used to visualize the available data. The module is available in the repository (hedera_covid.py), the data as well. We periodically update the datasets with those available online.

import numpy as np
from hedera_covid import DataHandler, plot_death_rate, plot_daily_cases, plot_confirmed_cases

# load data
path_confirmed = '../../Data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
path_death = '../../Data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'

First of all, we create an object of type DataHandler. This class can be used to perform internally operation on the dataset, so that data are afterwards ready and clean for plotting.

covid_data = DataHandler(data_confirmed_path = path_confirmed,
                         data_death_path = path_death)

covid_data.confirmed.head()

	Province/State	Country/Region	Lat	Long	...	3/31/20	4/1/20	4/2/20	4/3/20	4/4/20	4/5/20	4/6/20	4/7/20	4/8/20	4/9/20
0	NaN	Afghanistan	33.0000	65.0000	...	174	237	273	281	299	349	367	423	444	484
1	NaN	Albania	41.1533	20.1683	...	243	259	277	304	333	361	377	383	400	409
2	NaN	Algeria	28.0339	1.6596	...	716	847	986	1171	1251	1320	1423	1468	1572	1666
3	NaN	Andorra	42.5063	1.5218	...	376	390	428	439	466	501	525	545	564	583
4	NaN	Angola	-11.2027	17.8739	...	7	8	8	8	10	14	16	17	19	19

5 rows × 83 columns

Initialize list of countries

Now, it is possible to get data of a country to the list that we want to look at. This can be done using

my_country = covid_data.get_country('Name of the country')

The Name of the country is the name given in the third column (Country/Region) of the covid_data dataframe.

For example:

italy = covid_data.get_country('Italy')

This create a dictionary with the following keys:

italy.keys()

dict_keys(['name', 'dates', 'confirmed', 'deaths', 'daily_new_cases', 'daily_deaths', 'start', 'start_death'])

We can also create a list of countries

covid_data.add_country('Name of the country')

that we can analyze together.

my_countries = ['Italy','Spain','Germany','Austria','France','United Kingdom','US','Sweden','Netherlands']

for c in my_countries:
    covid_data.add_country(c)

Starting from this object, we can now use some functions implemented in our module to visualize the data

Note: (this is an ongoing work!)

First, we have to collect the data. For the reported cases we use this function:

data = covid_data.get_confirmed_data(start_date=0,n_smooth=7,rescale=True)

Then we can create a plotly bar chart (for example) and display it.

# for plotly
from plotly.offline import iplot
from plotly.offline import init_notebook_mode, plot
from IPython.core.display import display, HTML
import plotly as py
import plotly.tools as tls
init_notebook_mode(connected=True)

fig = {
    "data": data,
    "layout": {"title": {"text": "Confirmed Cases (reascaled in each country)"}}
}

plot(fig, filename = 'figure.html')
display(HTML('figure.html'))

Remark

We can also load at once all available countries.

For this, we will first create a new handler (so that we do not mix up data):

# create first a new handler
covid_data_full = DataHandler(data_confirmed_path = path_confirmed,
                              data_death_path = path_death)

Now, we can get all names. Note that we have to use np.unique since there are countries that appear multiple times (this is how JHU stored the data)

# now get all names
all_country_names = covid_data.confirmed['Country/Region']
all_country_names = np.unique(all_country_names)
for n in all_country_names:
    covid_data_full.add_country(n)

# alternative
import plotly.graph_objects as go
init_notebook_mode(connected=True)

fig = go.Figure()

for c in covid_data_full.countries:
    fig.add_trace(go.Bar(x=c['dates'],
                         y=c['confirmed'],
                         name=c['name'],
                        )
                 )

fig.update_layout(
    title='# Confirmed Cases',
    xaxis_tickfont_size=14,
    yaxis=dict(
        title='Cases',
        titlefont_size=16,
        tickfont_size=14,
    ),
    legend=dict(
        x=0,
        y=1.0,
        bgcolor='rgba(255, 255, 255, 0)',
        bordercolor='rgba(255, 255, 255, 0)'
    ),
    barmode='group',
    bargap=0.15, # gap between bars of adjacent location coordinates.
    bargroupgap=0.1 # gap between bars of the same location coordinate.
)

plot(fig, filename = 'figure.html')
display(HTML('figure.html'))

We can use plotly to visualize all countries at once. Double-click on the legend name to isolate one single country.

Number of confirmed cases

Daily variation

The following figure shows the number of confirmed cases. Note that this is extremely dependent on the different testing protocols and capacity of different countries.

For this, we can use the function

plot_daily_cases(countries,start_date,n_smooth,rescale)

Parameters:

countries: a list of countries
start_date: day where the plot start (0 = January 22)
n_smooth: smoothing of the data (data will be averaged over n_smooth days, 7 is usually good)
rescale: set True if you want to rescale curves to the same start. In this case start_date won't be used.

import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 14})
plot_daily_cases(covid_data.countries,start_date=0,n_smooth=7,rescale=True)

Total cases (reported)

We can look at the total number of cases using the function

plot_confirmed_cases(countries,start_date,n_smooth,rescale)

Parameters:

countries: a list of countries
start_date: day where the plot start (0 = January 22)
n_smooth: smoothing of the data (data will be averaged over n_smooth days, 7 is usually good)
rescale: set True if you want to rescale curves to the same start. In this case start_date won't be used.
log_scale: set to True to scale use logarithmic scale for the y-axis

plot_confirmed_cases(covid_data.countries,start_date=30,n_smooth=0,rescale=False,log_scale=True)

Mortality Rate

We can look at the official mortality rate over time for a selected set of countries

import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 14})
plot_death_rate(covid_data.countries,start_date = 32)

Number of confirmed cases

The following figure shows the number of confirmed cases. Note that this is extremely dependent on the different testing protocols and capacity of different countries.

Use:

start_date to start plotting from a particular day (later than January 22)
n_smooth to smooth the data (7 is usually good)
rescale if you want to rescale curves to the same start. In this case start_date won't be used.

import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 14})
plot_daily_cases(covid_data.countries,start_date=0,n_smooth=7,rescale=True)